You Don’t Trust an AI Model You Can’t Explain

Explainable and trustworthy AI not only ensures transparency, fairness and regulatory compliance but also builds confidence within users, customers and regulators while unlocking real business value.

• Explainable AI (XAI) refers to models and algorithms designed to reveal how they reach decisions thereby making predictions transparent and understandable to humans.

• Trustworthy AI is the concept under which AI systems are trustworthy, reliable, fair, transparent, accountable and acting within ethical and legal environments.

 It is particularly in high-stakes industries such as finance, healthcare and criminal justice that the above distinction matters greatly. Risks associated with AI concerned 91% of executives in a recent survey by McKinsey who said they were not ready to use AI ‘safely and responsibly’. 40% of them identified opaque models as the topmost risk.

Consider healthcare: STAT News revealed that IBM’s Watson for Oncology frequently provided unsafe and inaccurate cancer treatment advice since its inner logic could not be verified. A ProPublica study in criminal justice showed that the COMPAS algorithm systematically overestimated recidivism for Black defendants. In finance, regulators require bank staff to ‘reasonably understand’ existing lending models, else risking bias and non-compliance.

In fact, unexplained AI may even be life-threatening, such as in several industry settings. BCG cautions that a field-service AI failure "can mean a stalled train, a disabled emergency power system in a hospital...trust in AI isn't optional – it's essential".

In fact, field technicians are using AI more and more to service critical infrastructure. Without explainability, and therefore trust, they may be forced to disregard its recommendations. According to BCG, technicians should be aware of why AI made an offer to make it a real action point.

For example, when a global airline incorporated explainable AI into its engine maintenance system – including a display of confidence levels and other factors that influence each prediction – forecasting accuracy increased 30% and repair-shop productivity by 15%. Operational performance can directly be enhanced when AI is made clear and understandable.

Stakeholder and Regulatory Obligations to Transparent AI

- Regulations:: Governments and regulators are already demanding increased AI transparency. The EU AI Act, for example, already requires the disclosure of logic and limitations of high-risk AI systems (hiring or loan-approval algorithms, etc.).

Gartner forecasts that by 2026 half of the world’s governments will establish protocols for ‘responsible AI’ through laws and policies that focus on ethics, transparency and data privacy. Practically, this implies companies that apply AI in the financial sector, healthcare or government services should implement audit trails and explainability right at initial stages or risk being fined or sanctioned.

- Customer and Partner Trust: Repeated surveys have shown that end-users and clients do not implement tools they do not have the capability to audit. PwC discovered responsible AI practices bring success to a company, including "improved transparency" and better risk management. These contribute positively in garnering stakeholder trust.

Customers and business executives are increasingly demanding firms justify AI-based decisions (in credit scoring, insurance, underwriting, etc.) to ensure fairness.

- Internal Governance: Boards, investors and managers are increasing internal pressures on explainable models. According to a recent Deloitte poll, the biggest objection to the implementation of advanced AI in finance was the lack of trust. This has furthered the introduction of strict AI governance systems in firms.

The Trustworthy AI model created by Deloitte outlines seven principles, from transparency and fairness to privacy and accountability, that have to be integrated into an AI lifecycle. These guardrails are useful in assisting organizations to audit their decisions, detect bias and prove compliance to regulators and auditors.

Risks  of Opaque AI and the Benefits of Explainability

Opaque ‘black-box’ AI carries with it very real risks. When models make decisions without justification, errors often go unnoticed and result in either damages or scandals. We have already seen instances of backlash: facial-recognition systems that wrongly identified individuals, loan algorithms that unwillingly coded racial discrimination into mortgage-grants, etc.

In healthcare, a transparency deficiency may literally cost lives – as was the case with IBM’s Watson – as well as bring lawsuits or breed mistrust. Hidden model behavior almost guarantees regular regulatory infractions or irreparable tarnished reputations if not caught early.

In contrast, explainable AI leads to value creation on various levels. Users and decision-makers will never embrace AI tools they do not trust. Transparency, as BCG explained, leads to trust and, as a result, to usage.

In fact, according to the research conducted by PwC, companies that have invested in responsible AI were found to have increased transparency, improved risk management and reduced disruptions, all of which fostered stakeholder confidence and increased adoption rates.

The airline example is a case of clear ROI. The example also serves as a good reminder of how explainability serves to prevent expenses too, by enabling teams to understand and diagnose model weaknesses, (thereby improving their performance with time) and ensure fairness (identifying features which introduce bias).

Explainability and fairness have in fact become the focus of compliance avoidance by not only minimizing the risk of costly mistakes, but also maximizing the bottom-line value of AI initiatives.

Credible AI doesn’t just tick an ethics box – it’s good business.